The increase in the amount of data needed to be analyzed in medical imaging applications makes the existing manual, plane by plane image analysis method inefficient and, in certain cases, ineffective. This fact combined with the advances in digital image processing hardware and algorithms such as fast volume rendering and temporal image processing that can ease the analysis task have further increased the demand for medical image processing systems. Because of the short processing time and, in most cases, interactivity requirements of medical image processing applications, such systems must have very high performance levels. On the other hand, since such a system is typically an accessory to a host system, the medical image processing system should cost much less than the host system.
New medical image processing system designs are using parallel processors in an attempt to increase data processing speed. However, such systems are expensive and, because many processors are involved, are generally difficult to program by the user. Consequently, there is a continuing need for adaptable image processing systems which have a reduced price/performance ratio.